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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import os.path as osp
from .operators import *
from .batch_operators import BatchRandomResize, BatchRandomResizeByShort, _BatchPad
from paddlers import transforms as T
def decode_image(im_path,
to_rgb=True,
to_uint8=True,
decode_bgr=True,
decode_sar=True,
read_geo_info=False):
"""
Decode an image.
Args:
im_path (str): Path of the image to decode.
to_rgb (bool, optional): If True, convert input image(s) from BGR format to
RGB format. Defaults to True.
to_uint8 (bool, optional): If True, quantize and convert decoded image(s) to
uint8 type. Defaults to True.
decode_bgr (bool, optional): If True, automatically interpret a non-geo
image (e.g. jpeg images) as a BGR image. Defaults to True.
decode_sar (bool, optional): If True, automatically interpret a two-channel
geo image (e.g. geotiff images) as a SAR image, set this argument to
True. Defaults to True.
read_geo_info (bool, optional): If True, read geographical information from
the image. Deafults to False.
Returns:
np.ndarray|tuple: If `read_geo_info` is False, return the decoded image.
Otherwise, return a tuple that contains the decoded image and a dictionary
of geographical information (e.g. geographical transform and geographical
projection).
"""
# Do a presence check. osp.exists() assumes `im_path` is a path-like object.
if not osp.exists(im_path):
raise ValueError(f"{im_path} does not exist!")
decoder = T.DecodeImg(
to_rgb=to_rgb,
to_uint8=to_uint8,
decode_bgr=decode_bgr,
decode_sar=decode_sar,
read_geo_info=read_geo_info)
# Deepcopy to avoid inplace modification
sample = {'image': copy.deepcopy(im_path)}
sample = decoder(sample)
if read_geo_info:
return sample['image'], sample['geo_info_dict']
else:
return sample['image']
def build_transforms(transforms_info):
transforms = list()
for op_info in transforms_info:
op_name = list(op_info.keys())[0]
op_attr = op_info[op_name]
if not hasattr(T, op_name):
raise ValueError(
"There is no transform operator named '{}'.".format(op_name))
transforms.append(getattr(T, op_name)(**op_attr))
eval_transforms = T.Compose(transforms)
return eval_transforms